{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setting specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "global graph_opts1 bgcolor(white) graphregion(color(white)) legend(region(lc(none) fc(none))) ///\n",
    "\tylab(,angle(0) nogrid) title(, justification(left) color(black) span pos(11)) subtitle(, justification(left) color(black))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Uploading data (.dta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "use \"https://github.com/SaoriIwa/Stata-IE-Visual-Library/raw/master/Library/Box%20plots/10-25-50-75-90%20Percentile%20box%20plot/data.dta\", clear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Creating variables for each percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "collapse (p10) p10=competence_mle (p25) p25=competence_mle (p50) p50=competence_mle ///\n",
    "\t(p75) p75=competence_mle (p90) p90=competence_mle ///\n",
    "\t, by(provider_cadre country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Reshaping the data set \n",
    "\n",
    "Keeping ``provider_cadre`` and ``Country`` in columns, the command takes values from variables starting with ``p`` followed by numbers. (``p10``, ``p50``, etc.) It creates a column ``p`` for the values.  \n",
    "\n",
    "Without specifying the name of ``j()``, stata automatically creates ``_j`` column, and put the number part of the variable names (25, 50, 75 and 90) that correspond with the value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "qui reshape long p, i(provider_cadre country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Renaming the values of ``country`` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "qui replace country = \"Kenya (N = 372)\" if regexm(country,\"Kenya\")\n",
    "qui replace country = \"Madagascar (N = 588)\" if regexm(country,\"Madagascar\")\n",
    "qui replace country = \"Nigeria (N = 1,579)\" if regexm(country,\"Nigeria\")\n",
    "qui replace country = \"Tanzania (N = 224)\" if regexm(country,\"Tanzania\")\n",
    "qui replace country = \"Uganda (N = 432)\" if regexm(country,\"Uganda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Creating the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "This front-end cannot display the desired image type."
      ]
     },
     "metadata": {
      "image/png": {
       "height": 436,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph box p ///\n",
    "\t,  hor over(provider_cadre) over(country ) ///\n",
    "\tlegend(order(0 \"Professional Cadre:\" 1 \"Medical Officer\" 2 \"Nurse\") r(1) symxsize(small) symysize(small)  pos(6) ring(1)) ///\n",
    "\tasy $graph_opts1 ylab(-1 \"-1 SD\" 0 \"SDI Mean\" .553483 \"Median\" 1 \"+1 SD\" 2 \"+2 SD\" 3 \"+3 SD\", labsize(vsmall)) ytit(\"\") note(\"\") ///\n",
    "\tlintensity(.5) yline(.553483 , lc(black) lp(dash)) ///\n",
    "\tbox(1 , fi(0) lc(maroon) lw(medthick)) box(2, fc(white) lc(navy) lw(medthick))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Exporting the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "qui graph export \"figure.png\" , replace width(1000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Stata",
   "language": "stata",
   "name": "stata"
  },
  "language_info": {
   "file_extension": ".do",
   "mimetype": "text/x-stata",
   "name": "stata"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
